Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99024
Title: Efficient HIK SVM learning for image classification
Authors: Wu, Jianxin
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Wu, J. (2012). Efficient HIK SVM Learning for Image Classification. IEEE Transactions on Image Processing, 21(10), 4442-4453.
Series/Report no.: IEEE transactions on image processing
Abstract: Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms. This paper presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.
URI: https://hdl.handle.net/10356/99024
http://hdl.handle.net/10220/13501
ISSN: 1057-7149
DOI: 10.1109/TIP.2012.2207392
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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